Deploying Custom ML Docker Containers on AWS Lambda

Deploying machine learning models often hits a wall when dealing with library sizes and dependency conflicts. Moving beyond standard serverless limits requires a modern containerized approach. This text-based course guides you through the process of containerizing your machine learning models and deploying them seamlessly on AWS Lambda. You will transition from writing local ML scripts to deploying robust, production-ready serverless APIs that scale automatically. What you'll learn: - Understand foundational serverless and container concepts, including AWS Lambda limits and Docker basics. - Build optimized Docker images specifically tailored for machine learning workloads. - Manage and push container images securely using AWS Elastic Container Registry (ECR). - Configure AWS Lambda to run custom containerized models with appropriate memory and timeout settings. - Apply modern MLOps practices such as multi-stage builds to keep image sizes small and efficient. - Test and debug your serverless ML endpoints using structured logging and basic observability tools. You will start with core containerization concepts before moving step-by-step through configuration, deployment, and testing. Every concept is explained through clear text explanations and structured code snippets. This course is designed for beginner cloud engineers, data scientists, and developers looking to deploy ML models without managing complex infrastructure, with no prior AWS or Docker experience required. Start reading today to master serverless machine learning deployment.